1. Field of the Invention
The present invention relates to a target tracker, and particularly, to a target tracker adapted to track a target object in a time series of frames of image data.
2. Description of the Related Arts
There are practical applications of camera systems making use of images picked up by a camera in a variety of situations, as represented by a picture monitoring system or video conference system. Some of such camera systems have a tracking function of automatically tracking a preset target object, to pick it up, while changing an imaging region of camera. For instance, there are picture monitoring systems provided with a tracking function and adapted, once a suspicious person is caught as a target object, to continue imaging, while tracking the person to pick up in a picture. Further, there are video conference systems provided with a tracking function and adapted to pick up conference images tracking a focused person.
Tracking a target object to pick up images thereof needs three camera to have a pan, tilt, zoom, and the like controlled in accordance with a displacement of the object, to keep this within an angle of field to be imaged. For the implementation, there should be recognition of a target object in images, to detect a direction of displacement of the same.
As techniques for recognition of a target object in images to detect a displacement direction thereof, there were various methods employed in the past, including a frame difference method or a background difference method making use of a difference in luminance, while instead, in recent years there have been studies on target tracking techniques using a particle filter, as disclosed in the patent document 1 (Japanese Patent Application Laid-Open Publication No. 2004-282535) and the non-patent document 1 (CONDENSATION—conditional density propagation for visual tracking, M. Isard, and A. Blake, Int. J. Computer vision, vol. 28, No. 1, pp. 5-28 (1998)).
The particle filter is a technique for approximate calculation by a Bayesian filter making use of a posterior probability, which employs a finite number of particles for expression of a function of probability distribution to predict a time sequence. In other words, the particle filter, being a sort of sequential Mote Carlo method based on a sampling, makes approximation of a time series of distributions in terms of sets of particle positions and weights, allowing for a tracking even of such a distribution that will not be approximated by a Gaussian. Further, it allows various amounts of characteristics of time series to be handled as likelihoods, and has a wide range of applications. In an application to a tracking of target object, there have been measurements of likelihoods using a color of target object, as disclosed in the patent document 1, as well. In this case, likelihoods were measured depending on how many pixels residing within vicinities of particles are approximate in color to the color of target object, with results of measurements affording to estimate a position of target object.